Effect of different methods of cutting fluid application on turning of a difficult-to-machine steel (SAE EV-8)

In this study, different methods of cutting fluid application are used in turning of a difficult-to-machine steel (SAE EV-8). Initially, a semisynthetic cutting fluid was applied using a conventional method (i.e. overhead flood cooling), minimum quantity of cutting fluid, and pulverization. A lubricant of vegetable oil (minimum quantity of lubricant) was also applied using the minimum quantity method. Thereafter, a cutting fluid jet under high pressure (3.0 MPa) was singly applied in the following regions: chip-tool interface, top surface of the chip (between workpiece and chip) and tool-workpiece contact. Moreover, two other methods were used: an interflow between conventional application and chip-tool interface jet (combined method) and, finally, three jets simultaneously applied. In order to carry out these tests, it was necessary to set up a high-pressure system using a piston pump for generating a cutting fluid jet, a venturi for fluid application (minimum quantity of cutting fluid and minimum quantity of lubricant) and a nozzle for cutting fluid pulverization. The output variables analyzed included tool life, surface roughness, cutting tool temperature, cutting force, chip form, chip compression rate and machined specimen microstructure. Among the results, it can be observed that the tool life increases and the cutting force decreases with the application of cutting fluid jet, mainly when it is directed to the chip-tool interface. Excluding the methods involving jet fluid, the conventional method seems to be more efficient than other methods of low pressure, such as minimum quantity of volume and pulverization, when considering just the cutting tool wear. © 2013 IMechE.

Different methods of cutting fluid application on turning of a difficult-to-machine steel

Different methods of cutting fluid application are used on turning of a difficult-tomachine steel (SAE EV-8). A semi-synthetic cutting fluid was applied using a conventional method, minimum quantity of cutting fluid (MQCF), and pulverization. By the minimum quantity method was also applied a lubricant of vegetable oil (MQL). Thereafter, a cutting fluid jet under high pressure (3.0 MPa) was singly applied in the following regions: chip-tool interface; top surface of the chip; and tool-workpiece contact. Two other methods were used: an interflow between conventional application and chip-tool interface jet and, finally, three jets simultaneously applied. In order to carry out these tests, it was necessary to set up a high pressure system using a piston pump for generating a cutting fluid jet, a Venturi for fluid application (MQCF and MQL), and a nozzle for cutting fluid pulverization. The output variables analyzed included tool life, surface roughness, cutting tool temperature, cutting force, chip form, chip compression rate and machined specimen microstructure. It can be observed that the tool life increases and the cutting force decreases with the application of cutting fluid jet, mainly when it is directed to the chip-tool interface. Excluding the methods involving jet fluid, the conventional method seems to be more efficient than other methods of low pressure. © (2013) Trans Tech Publications, Switzerland.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Machine vision to identify broiler breeder behavior

Animal behavioral parameters can be used to assess welfare status in commercial broiler breeders. Behavioral parameters can be monitored with a variety of sensing devices, for instance, the use of video cameras allows comprehensive assessment of animal behavioral expressions. Nevertheless, the development of efficient methods and algorithms to continuously identify and differentiate animal behavior patterns is needed. The objective this study was to provide a methodology to identify hen white broiler breeder behavior using combined techniques of image processing and computer vision. These techniques were applied to differentiate body shapes from a sequence of frames as the birds expressed their behaviors. The method was comprised of four stages: (1) identification of body positions and their relationship with typical behaviors. For this stage, the number of frames required to identify each behavior was determined; (2) collection of image samples, with the isolation of the birds that expressed a behavior of interest; (3) image processing and analysis using a filter developed to separate white birds from the dark background; and finally (4) construction and validation of a behavioral classification tree, using the software tool Weka (model 148). The constructed tree was structured in 8 levels and 27 leaves, and it was validated using two modes: the set training mode with an overall rate of success of 96.7%, and the cross validation mode with an overall rate of success of 70.3%. The results presented here confirmed the feasibility of the method developed to identify white broiler breeder behavior for a particular group of study. Nevertheless, more improvements in the method can be made in order to increase the validation overall rate of success. (C) 2013 Elsevier B.V. All rights reserved.

Hot turning of a difficult-to-machine steel (sae xev-f) aided by infrared radiation

A possible way for increasing the cutting tool life can be achieved by heating the workpiece in order to diminish the shear stress of material and thus decrease the machining forces. In this study, quartz electrical resistances were set around the workpiece for heating it during the turning. In the tests, heat-resistant austenitic alloy steel was used, hardenable by precipitation, mainly used in combustion engine exhaustion valves, among other special applications for industry. The results showed that in the hot machining the cutting tool life can be increased by 340% for the highest cutting speed tested and had a reduction of 205% on workpiece surface roughness, accompanied by a force decrease in relation to conventional turning. In addition, the chips formed in hot turning exhibited a stronger tendency to continuous chip formation indicating less energy spent in material removal process. Microhardness tests performed in the workpieces subsurface layers at 5 m depth revealed slightly higher values in the hot machining than in conventional, showing a tendency toward the formation of compressive residual stress into plastically deformed layer. The hot turning also showed better performance than machining using cutting fluid. Since it is possible to avoid the use of cutting fluid, this machining method can be considered better for the environment and for the human health.

Introducing the Discriminative Paraconsistent Machine (DPM)

This paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency.

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)